Within the concept of physical human-robot interaction (pHRI), the most important criterion is the safety of the human operator interacting with a high degree of freedom (DoF) robot. Therefore, a robust control scheme is in high demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In this paper, an adaptive decentralized control strategy is designed to accomplish the abovementioned objectives. To do so, a human upper limb model and an exoskeleton model are decentralized and augmented at the subsystem level to enable a decentralized control action design. Moreover, human exogenous force (HEF) that can resist exoskeleton motion is estimated using radial basis function neural networks (RBFNNs). Estimating both human upper limb and robot rigid body parameters, along with HEF estimation, makes the controller adaptable to different operators, ensuring their physical safety. The barrier Lyapunov function (BLF) is employed to guarantee that the robot can operate in a safe workspace while ensuring stability by adjusting the control law. Unknown actuator uncertainty and constraints are also considered in this study to ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole system is established by means of the virtual stability concept and virtual power flows (VPFs) under the proposed robust controller. The experimental results are presented and compared to proportional-derivative (PD) and proportional-integral-derivative (PID) controllers. To show the robustness of the designed controller and its good performance, experiments are performed at different velocities, with different human users, and in the presence of unknown disturbances. The proposed controller showed perfect performance in controlling the robot, whereas PD and PID controllers could not even ensure stable motion in the wrist joints of the robot.

翻译：在物理人机交互（pHRI）概念中，与高自由度（DoF）机器人交互的操作员安全是最重要的准则。因此，亟需一种鲁棒控制方案来建立安全的pHRI并稳定非线性高自由度系统。本文设计了一种自适应分散控制策略以实现上述目标。为此，将人体上肢模型和外骨骼模型在子系统层面进行分散与增广，从而实现分散控制作用设计。此外，利用径向基函数神经网络（RBFNNs）估计可能阻碍外骨骼运动的人体外生作用力（HEF）。通过同时估计人体上肢和机器人刚体参数以及HEF，使控制器能够适应不同操作员，保障其物理安全。采用障碍李雅普诺夫函数（BLF）确保机器人可在安全工作空间内运行，并通过调整控制律保证稳定性。本研究还考虑了未知的致动器不确定性和约束条件，以实现平滑安全的pHRI。随后，借助虚拟稳定性概念和虚拟功率流（VPFs），在所提出的鲁棒控制器下建立了整个系统的渐近稳定性。通过实验验证并将结果与比例微分（PD）和比例积分微分（PID）控制器进行对比。为展示所设计控制器的鲁棒性及良好性能，在不同速度、不同操作员以及存在未知干扰的条件下进行了实验。所提出的控制器在机器人控制中表现出卓越性能，而PD和PID控制器甚至无法确保机器人腕关节的稳定运动。